A Canadian Perspective on Patient Experience using Virtual Care During COVID-19
Bibliographic record
Abstract
The COVID-19 pandemic has necessitated a rapid change in the delivery of healthcare around the world. Many facilities have transitioned suitable services to virtual care to reduce the risk of viral transmission and preserve healthcare resources for spikes in COVID-19 cases. Since institutions have rapidly expanded the usage of virtual care beyond its previous confines, investigations are required to ensure that the adapted system is working for patients. While important, clinical and patient-reported outcome data do not provide complete insight into the specific impacts of pandemic-time changes from the patient’s perspective. Therefore, to get a complete picture of these changes, it is also necessary to look at patient experience, which evidence suggests, could be impacted by virtual care in positive ways, but only in specific cases. Thus, it is vital to record pandemic-time patient experiences and analyse how the implementation of virtual visits impacts the delivery of person-centred care. This data should be used to determine how virtual care can be optimally implemented into the Canadian healthcare system after the resolution of the COVID-19 pandemic. Although it is currently unclear how virtual care will be integrated into the post-pandemic landscape, the approach offers benefits to both patients and providers. Canada-wide, longitudinal studies investigating patient experience using virtual care during the COVID-19 pandemic are required in order to ascertain exactly how this novel approach can be leveraged to benefit patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".